A 250 m annual alpine grassland AGB dataset over the Qinghai-Tibet Plateau (2000-2019) in China based on in situ measurements, UAV photos, and MODIS data
文献类型：期刊论文
| 作者 | Zhang, Huifang4,5; Tang, Zhonggang5; Wang, Binyao5; Kan, Hongcheng5; Sun, Yi5; Qin, Yu4; Meng, Baoping5; Li, Meng5; Chen, Jianjun3; Lv, Yanyan5 |
| 刊名 | EARTH SYSTEM SCIENCE DATA
 |
| 出版日期 | 2023-02-14 |
| 卷号 | 15期号:2页码:821-846 |
| DOI | 10.5194/essd-15-821-2023 |
| 文献子类 | Article |
| 英文摘要 | The alpine grassland ecosystem accounts for 53 % of the Qinghai-Tibet Plateau (QTP) area and is an important ecological protection barrier, but it is fragile and vulnerable to climate change. Therefore, continuous monitoring of grassland aboveground biomass (AGB) is necessary. Although many studies have mapped the spatial distribution of AGB for the QTP, the results vary widely due to the limited ground samples and mismatches with satellite pixel scales. This paper proposed a new algorithm using unmanned aerial vehicles (UAVs) as a bridge to estimate the grassland AGB on the QTP from 2000 to 2019. The innovations were as follows: (1) in terms of ground data acquisition, spatial-scale matching among the traditional ground samples, UAV photos, and MODIS pixels was considered. A total of 906 pairs between field-harvested AGB and UAV sub-photos and 2602 sets of MODIS pixel-scale UAV data (over 37 000 UAV photos) were collected during 2015-2019. Therefore, the ground validation samples were sufficient and scale-matched. (2) In terms of model construction, the traditional quadrat scale (0.25 m(2)) was successfully upscaled to the MODIS pixel scale (62 500 m2) based on the random forest and stepwise upscaling methods. Compared with previous studies, the scale matching of independent and dependent variables was achieved, effectively reducing the impact of spatial-scale mismatch. The results showed that the correlation between the AGB values estimated by UAV and MODIS vegetation indices was higher than that between field-measured AGB and MODIS vegetation indices at the MODIS pixel scale. The multi-year validation results showed that the constructed MODIS pixel-scale AGB estimation model had good robustness, with an average R-2 of 0.83 and RMSE of 34.13 g m(-2). Our dataset provides an important input parameter for a comprehensive understanding of the role of the QTP under global climate change. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (; H. Zhang et al., 2022). |
| WOS关键词 | ABOVEGROUND BIOMASS ; VEGETATION INDEXES ; GEOSTATISTICAL APPROACH ; AUTOMATED CROP ; IMAGERY ; SOIL ; ALGORITHMS ; PATTERN ; COVER ; LEAF |
| WOS研究方向 | Geology ; Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:000930551700001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200747]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Guilin Univ Technol, Coll Geomat & Geoinformat, 12 Jiangan Rd, Guilin 541004, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China 3.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, State Key Lab Cryospher Sci, 320 Donggang West Rd, Lanzhou 730000, Peoples R China 4.Nantong Univ, Sch Geog Sci, 999 Tongjing Rd, Nantong 226007, Jiangsu, Peoples R China 5.Nantong Univ, Inst Fragile Ecoenvironm, 999 Tongjing Rd, Nantong 226007, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Huifang,Tang, Zhonggang,Wang, Binyao,et al. A 250 m annual alpine grassland AGB dataset over the Qinghai-Tibet Plateau (2000-2019) in China based on in situ measurements, UAV photos, and MODIS data[J]. EARTH SYSTEM SCIENCE DATA,2023,15(2):821-846. |
| APA | Zhang, Huifang.,Tang, Zhonggang.,Wang, Binyao.,Kan, Hongcheng.,Sun, Yi.,...&Yi, Shuhua.(2023).A 250 m annual alpine grassland AGB dataset over the Qinghai-Tibet Plateau (2000-2019) in China based on in situ measurements, UAV photos, and MODIS data.EARTH SYSTEM SCIENCE DATA,15(2),821-846. |
| MLA | Zhang, Huifang,et al."A 250 m annual alpine grassland AGB dataset over the Qinghai-Tibet Plateau (2000-2019) in China based on in situ measurements, UAV photos, and MODIS data".EARTH SYSTEM SCIENCE DATA 15.2(2023):821-846. |
入库方式： OAI收割
来源：地理科学与资源研究所
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